Application of system dynamics

Science of the Total Environment 527–528 (2015) 80–90

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Searching for solutions to mitigate greenhouse gas emissions by agricultural policy decisions — Application of system dynamics modeling for the case of Latvia Elina Dace a,⁎, Indra Muizniece a, Andra Blumberga a, Fabio Kaczala b a b

Institute of Energy Systems and Environment, Riga Technical University, Azenes 12/1, Riga LV1048, Latvia Department of Biology and Environmental Science, Faculty of Health & Life Sciences, Linnaeus University, SE-39182 Kalmar, Sweden

H I G H L I G H T S • • • • •

A system dynamics model is developed for estimating agricultural GHG emissions. Effect of decisions and measures on agricultural GHG emissions is assessed. Feedback links of an agricultural system are demonstrated. A limited number of options exist for limiting agricultural GHG emissions. Reaching GHG abatement targets will be challenging for Latvia.

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Article history: Received 16 February 2015 Received in revised form 22 April 2015 Accepted 24 April 2015 Available online xxxx Editor: D. Barcelo Keywords: Agricultural systems Emission mitigation Greenhouse gas emissions Manure management Modeling complex systems Nitrogen tax Policy System dynamics

a b s t r a c t European Union (EU) Member States have agreed to limit their greenhouse gas (GHG) emissions from sectors not covered by the EU Emissions Trading Scheme (non-ETS). That includes also emissions from agricultural sector. Although the Intergovernmental Panel on Climate Change (IPCC) has established a methodology for assessment of GHG emissions from agriculture, the forecasting options are limited, especially when policies and their interaction with the agricultural system are tested. Therefore, an advanced tool, a system dynamics model, was developed that enables assessment of effects various decisions and measures have on agricultural GHG emissions. The model is based on the IPCC guidelines and includes the main elements of an agricultural system, i.e. land management, livestock farming, soil fertilization and crop production, as well as feedback mechanisms between the elements. The case of Latvia is selected for simulations, as agriculture generates 22% of the total anthropogenic GHG emissions in the country. The results demonstrate that there are very limited options for GHG mitigation in the agricultural sector. Thereby, reaching the non-ETS GHG emission targets will be very challenging for Latvia, as the level of agricultural GHG emissions will be exceeded considerably above the target levels. Thus, other non-ETS sectors will have to reduce their emissions drastically to “neutralize” the agricultural sector's emissions for reaching the EU's common ambition to move towards low-carbon economy. The developed model may serve as a decision support tool for impact assessment of various measures and decisions on the agricultural system's GHG emissions. Although the model is applied to the case of Latvia, the elements and structure of the model developed are similar to agricultural systems in many countries. By changing numeric values of certain parameters, the model can be applied to analyze decisions and measures in other countries. © 2015 Elsevier B.V. All rights reserved.

1. Introduction It is expected that with the increase of global population, issues of food safety and agriculture intensification will become highly topical in the near future. Projecting these changes, it is important to find

⁎ Corresponding author at: Azenes 12/1, Riga LV1048, Latvia. E-mail address: [email protected] (E. Dace).

http://dx.doi.org/10.1016/j.scitotenv.2015.04.088 0048-9697/© 2015 Elsevier B.V. All rights reserved.

sustainable agricultural policies that will not only provide the world's population with quality food, but also not endanger the global climate. Agriculture is directly associated with climate change issues on environmental, economic and social dimensions. Climate influences agricultural productivity, whereas agricultural activities emit greenhouse gases (GHG). In 2010, the annual global non-CO2 GHG emissions from agriculture were estimated to be 5.2–5.8 Gt CO2 eq. (IPCC, 2014). That comprised about 10–12% of the total global anthropogenic emissions (IPCC, 2014).

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EU Member States have agreed to limit their GHG emissions from sectors not covered by the EU Emissions Trading Scheme (non-ETS) on average by − 10% in 2020 as compared to the emissions in 2005 (EC, 2009). That includes also emissions from agricultural sector (enteric fermentation, manure storage and management, soil fertilization, crop production etc.). In addition, Europe's ambition to move towards low-carbon economy by 2050 (EC, 2013) implies that by 2030 the non-ETS GHG emissions would have to be reduced by 30%, compared to 2005 levels (EC, 2014). A number of GHG mitigation options exist for agriculture. Moreover, according to the Fifth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC) (IPCC, 2014), many of the options can be easily implemented with high (N10–15%) emissions' reduction potential. Estimates by Smith et al. (2008) have shown that the agricultural GHG emissions have the technical annual reduction potential of approximately 5500–6000 Mt CO2 eq. In addition, Smith et al. (2008) have indicated that many of the options can be implemented with a relatively low cost, or even generate considerable benefits as increased agricultural production efficiency. Still, as the number of options is so large, it is difficult to assess which solution would provide the highest potential of GHG emissions' abatement for a particular location. Moreover, full impact of the various options on agricultural systems and their GHG emissions is unclear. The IPCC has established a methodology for assessment of GHG emissions from various industry sectors, including agriculture (IPCC, 2006). Even more, IPCC has achieved that governments recognize and involve into climate change mitigation. For assisting in GHG assessment, various tools have been developed that provide a framework and a database of emission factors (Colomb et al., 2013). Denef et al. (2012) have provided a comprehensive overview of publicly accessible tools related to agricultural and forestry GHG accounting. They have classified the tools into calculators, protocols and guidelines, and process-based models, and described the methodology, application, targeted users, inputs/outputs, and underlying database/data sources of each tool. There are also studies that have presented GHG assessment results by using other methods, e.g. an input–output analysis for investigating GHG emissions embodied in national economies, including agriculture (e.g. Chen and Zhang, 2010; Minx et al., 2009; Su et al., 2010; Zhang et al., 2015). Nevertheless, most of the developed tools and methods provide limited options for policy assessment. Also, the IPCC methodology facilitates making an inventory of GHG emissions to assess the trends and success achieved in the emissions' mitigation in the past. Whereas the forecasting options are limited, especially when policies and their interaction with the target system are tested. Some studies have put serious effort to fill the gap by creating advanced tools for policy assessment in the agricultural systems. For example, Schäfer and Neufeldt (2006) and Neufeldt and Schäfer (2008) have used a combined GIS-linear programming model to simulate the effects of various agricultural mitigation policies on GHG abatement potentials and their cost efficiencies in a German federal state. Though region-specific, the approach and model might be adaptable for other locations with similar properties. Many studies (e.g. Bockel et al., 2012; De Cara and Jayet, 2011; MacLeod et al., 2010) have used marginal abatement cost curves for modeling emissions' abatement potential and costs for individual technologies and measures. Often, also modeling tools as Markal/ TIMES, GAINS, and Primes are used to assist decision makers in selecting the appropriate policy measures. Still, the models are created primary for energy systems' modeling, not agriculture in particular. In addition, the models are based on linear programming and rarely consider nonlinearities of such complex systems as is agriculture. The aim of our study is to fill the gap by developing an advanced tool for policy assessment in the agricultural system. We do this by applying the system dynamics (SD). It is a modeling tool appropriate for policy design (Hjorth and Bagheri, 2006; Vizayakumar, 1995) since it allows for creating transparent models of complex dynamic systems characterized by accumulations (delays) and non-linear

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feedback mechanisms that explicitly reflect the relationship between cause and effect (Dace et al., 2014). SD was developed by Jay Forrester in 1950s (Forrester, 1958) and has, since then, been applied to a diverse set problems (Sterman, 2000), e.g. collection of waste portable batteries (Blumberga et al., 2015), design for biodiesel policies (Barisa et al., 2015), switching to renewable energy sources (Romagnoli et al., 2014), etc. The purpose of applying SD is to determine the structural origin of some identified problem behavior and to design and assess favorable policies that may effectively govern the system in the future (Saleh et al., 2010). SD has been applied for modeling agricultural systems, as well. One of the first studies was by Gupta and Kortzfleisch (1987) who have investigated how investment in agriculture would influence the performance of the overall agricultural system in developing countries. They also investigated how gross domestic product related to the changes in agricultural production would change. Since then several other studies applying SD for modeling agricultural systems have been conducted. Fisher et al. (2000) have developed a SD model to demonstrate the SD's applicability in decision making by managers of agricultural business and to evaluate potential adoption rates and diffusion patterns of technological advances. They have emphasized the complexity of the agricultural industry. Shi and Gill (2005) and later also Rozman et al. (2012) and Li et al. (2012) have applied the SD methodology for exploring environmental, economic, social and institutional effects of ecological agriculture. They use the developed models for analyzing various policy scenarios that advance the development of organic farming. The studies reveal the interaction among the agricultural industry, policies and their effect, thereof allowing to assess the sustainability of the system. One of the most recent and profound studies has been conducted by Warner et al. (2013). They have used SD to create a model that helps in analyzing the land-use change depending on biofuel development and economic and social drivers related to it. Yet, the reviewed studies have not been aimed at an analysis of impact of agricultural systems on changes in GHG emission levels. Moreover, to our knowledge, there are no studies with SD application to agricultural systems that would have had assessed the impact of agricultural policies on GHG emissions. Thus, our study offers an alternative tool for modeling agricultural GHG emissions and ex ante assessment of policies, decisions and measures. We use Latvia as the case study. In 2012, Latvia had the third highest share of anthropogenic GHG emissions from agriculture among the 28 member states of the European Union (EU-28), i.e. 22.0%; while in EU28, on average the share was only 10.3% (EEA, 2014). In Latvia, that composes 29.4% of the non-ETS emissions. In CO2 equivalent, 67.6% of the emissions were released as nitrous oxide (N2O), while 32.4% — as methane (CH4) (LNIR, 2014). Latvia as the EU member state has agreed to limit its non-ETS GHG emissions to 17% increase in 2020 as compared to 2005 (EC, 2009). In 2030, the emissions should be reduced by 30% from the 2005 level (EC, 2014). Nevertheless, national strategic plans include a target value for management of the country's agricultural lands at the level of 95% by 2020, from the historical 71% in 2005 (NDP, 2012), which means that the amount of GHG emissions from agriculture will also increase. It forces to look for solutions that would help in reducing the emissions. Therefore, Latvia is a perfect case for our study. 2. Methodology 2.1. Description of the model A system dynamics (SD) model was developed based on the IPCC guidelines for national GHG inventories (IPCC, 2006). That way, the IPCC's methodology for assessing the GHG emissions from agriculture was integrated with the SD's provided ability to simulate various policies and their impact on the system. As can be seen from the logical framework of the developed approach (see Fig. 1), we started with the statement of the problem, which is based on the historical data of

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Fig. 1. Logical framework of the developed methodology.

GHG emission development, national strategic documents on the development of agricultural sector, and the targets set for non-ETS GHG emissions' abatement. We continued with formulation of a hypothesis by considering the national strategic development plans for agriculture, assumptions with respect to the potential future development of GHG emission changes from the agriculture, and the GHG emissions' abatement targets. Then, the SD model was built based on the IPCC guidelines for GHG inventories. The model was validated by comparing the simulation results with the historical data on GHG emissions from agriculture. Finally, various decisions and measures were selected and analyzed to evaluate their impact on changes in the GHG emission levels. The developed approach follows the SD theory on modeling complex systems: (i) formulation of a problem, (ii) creation of a dynamic hypothesis, (iii) analysis of the structure and behavior of the complex system by constructing its model, (iv) development and testing of policies, and (v) implementation through changing the mental model of the complex system under study. All together known also under the acronym “P'HAPI” (Problem, Hypothesis, Analysis, Policy, Implementation). The aim for applying SD is to determine, how the system causes the identified problematic behavior, and to find a policy that would manage the system. As SD is particularly suitable for the analysis of complex systems, it is well applicable for modeling climate policy in the agricultural sector. In more detail SD is explained by Sterman (2000), Hjorth and Bagheri (2006) and Mingers and White (2010). The model was constructed using components that typically make up agricultural systems in many countries, i.e. management of land, livestock and crop production, manure management and soil fertilization, decisions on manure management practices and choice of a crop

to produce. It can be seen from Fig. 2 that the components are interconnected in a complex system. In a country, a certain land stock or area of land is available for agricultural purposes — crop production and grazing. Livestock production is influenced by the land available for grazing and demand for animal products — meet, milk, eggs etc., which, on their turn, depend on the welfare of population usually expressed by the gross domestic product (GDP). With livestock production manure management has to be maintained to limit GHG emissions. Various technologies exist for managing manure, including manure storage with and without cover, lagoons, anaerobic digesters, and others. In the case of anaerobic digesters, biogas is generated for renewable energy production. The manure and effluent from anaerobic digesters are then used for fertilizing soil. Also, synthetic fertilizers are used for soil fertilization to enhance crop production. Crops are used for production of food, animal feed and renewable energy resources in the form of biogas, biomass and biofuels. Thus, when demand for renewable energy sources exists together with a high potential of economic feasibility, installation of anaerobic digesters, fermenters, gasifiers and other technologies is stimulated. Distribution of various crops produced depends on the demand and ratio between the price of the crop and its production costs — crops with higher potential net income are cultivated more intensely. Hence, demand for crops impacts the amount of fertilizers applied to soils to increase the crops' yield, whereas the soil fertility limits the crop production. The discussed components form the main sectors of our SD model. In Fig. 2, the dashed line indicates the boundaries of the study in terms of GHG emissions, i.e. we consider the emissions generated from the processes of enteric fermentation, manure management, soil fertilization for crop production, atmospheric deposition, leaching and runoff. Whereas, GHG emitted during production of renewable energy and fertilizers, food processing, and land-use change are outside the scope of our study, and therefore ignored. Also, factors influencing food consumption patterns are not analyzed in the study and are used as exogenous inputs. The numeric values of certain parameters were estimated from historic statistical data regarding the changes in the number of livestock species and area of various produced crops, amount of nitrogen necessary for standard and additional yield of various crops, and changes in the development of manure management systems. Also, data about the historic change in GDP and land management, as well as interviews with experts were used. All the information reflects the situation in Latvia. Still, the case-specific numeric values of those parameters may be changed to portray the situation in some other country as the structure of the model remains the same. Simulations were made for the time period from 2005 until 2030 to allow us to analyze the future development of agricultural system and its emissions under various policy decisions. In SD, the structure of the real-world system under study is conveniently built into a model using stock-and-flow diagrams to capture the essential accumulations and activities. Within the system, drivers of activity are represented as information-feedback processes (Bush et al., 2008). The obtained model of feedbacks creates the system's structure that determines the system's performance. The model of our study was developed in the Powersim Studio 8 software environment. The model consists of three interconnected sub-models. The submodel of management of agricultural land (see Fig. S1) yields the area of the total agricultural land managed and how this land is split between areas with conventional and organic farming practices. This is done to obtain accurate results of GHG emissions, as organic farms are allowed to use organic livestock manure for land fertilization only. The sub-model of soil fertilization and crop production (see Fig. S2) simulates the dynamics of changes in the area used for cultivation of various crops (wheat, barley, rye, oat, rape, buckwheat, maize, triticale and other cereals, potatoes, feed beet and other vegetables), their yield and amount of synthetic fertilizers applied for

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Fig. 2. Diagram of the structure of the SD model.

2.2. Validation of the model For modeling studies, availability and quality of data are always the key concerns. No model can completely represent the system under study. The aim of validating a model is to gain confidence that the model adequately presents the system under study and is acceptable for its intended use (Forrester, 1961). Hence, the aim of system dynamics modeling is to present the trend of dynamic behavior of the real system, not to give projection of exact values (Sterman, 2000). In other words, the emphasis is placed on relationships rather than on the precision of the simulated outcomes. Usually, structural and behavioral validation tests are performed. The structural validation is conducted by putting the model through a sequence of tests to evaluate the reasonableness of the equations within a model. In behavior validation or reference tests, a model's generated outcomes for major variables are compared with historical available data (Barlas, 1996). Thus, if a model reproduces data that closely reflect the observed past behavior of the real system, it gains credibility for representing the system and simulating the influence of potential decision interventions by e.g. policy instruments. Typically, structural validation is performed before behavior validation, as behavior output can have meaning only when sufficient reliability of the model's structure is achieved. In this study, the extreme conditions test, time step adequacy test, boundary adequacy test, and dimensional consistency test were performed to examine the structural validity of the developed model. The extreme conditions test was applied to investigate whether logical results are obtained using extreme parameter values even though they are ever so unrealistic. For example, if the price of a crop would be zero, then a rapid decrease of areas cultivated for the crop and amount of fertilizers used would be expected. The test results confirm this and other similar assumptions.

A time step adequacy test is necessary to avoid problems that may arise due to the choice of incorrect time step. According to Sterman (2000), the time step should be 1/4 to 1/10 of the smallest time constant used in the model, which is 1 month in our model. The time step in our model is 1/4 of a month or 7.5 days. The adequacy of the time step was tested by decreasing the time step to 1/8 of a month. The test results showed no significant changes in the simulation results, though the time of a simulation run increased considerably. Boundary and structure adequacy tests were conducted throughout the modeling process to assure that the model is appropriate for the given purpose. Finally, dimensional consistency test was conducted (provided by the software) and demonstrated that all units and dimensions are adequate. Thus, the testing results allowed us to gain confidence that the essential factors and parameters determining the dynamic behavior of the real system are included in the model and that the boundaries and level of detail describe the real system with sufficient granularity. To perform the behavior validation, the key variables of the model were compared to the historical data. In our case, the aim of the model was to calculate GHG emissions from various sectors of agriculture. Fig. 3 shows the comparison of simulated and historical data of the total GHG emissions in the agricultural sector in Latvia. The historical data were taken from the Latvia's National Inventory Report (LNIR, 2014) that was submitted under the United Nations Framework Convention on Climate Change and the Kyoto Protocol. It can be seen that

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reaching the yield. Finally, the sub-model of livestock and manure management contains two interlinked parts, i.e. determination of livestock number for estimating the amount of GHG emissions from enteric fermentation and assessment of manure management practices for estimating the amount of GHG emissions from manure management systems. A detailed description of the sub-models is provided in the Supplementary materials.

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Fig. 3. Historical and simulated data of the total GHG emissions in the agricultural sector in Latvia, 2005–2012.

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the simulated amount of GHG emissions reasonably match the historical GHG emissions' data. Finally, in order to determine the robustness of the model, a mean absolute percentage error (MAPE) analysis was applied to the values of historical and simulated GHG emissions (see Eq. (1)). MAPE ¼

 n   100 X Ai − F i  n i¼1  Ai 

ð1Þ

where Ai is the historic value, Fi is the simulated value, and n is the number of fitted points. The following results of the MAPE analysis were obtained: 0.88% for GHG emissions from enteric fermentation, 7.14% for GHG emissions from manure management, 0.17% for GHG emissions from agricultural soils, and 1.08% for the total GHG emissions in the agricultural sector. Thus, the developed model passed the behavior validity and sensitivity tests. The validation indicated that the model is capable of generating the desired right behavior for the right reasons. Thereof, the results obtained may be considered reliable and the model developed may be used effectively for in-depth policy simulation and analysis. 2.3. Simulated decisions and measures The policy measures affecting GHG emissions from agricultural systems can broadly be categorized into climate and non-climate policies, as both can have an impact on GHG mitigation (Smith et al., 2007). As indicated by Smith et al. (2005), in Europe, there has been little evidence of climate policy's effect on GHG emissions from agriculture, and the most effect has occurred via non-climate policy as, e.g. United Nations' conventions on biodiversity, focused transition to bioenergy, and agro-environmental schemes. (Smith et al., 2007). Yet, Neufeldt and Schäfer (2008) name several direct policy instruments that would be readily implementable for achieving reduction of agricultural GHG emissions, including nitrogen tax, emission caps and taxes and livestock extensification. Also, Feliciano et al. (2014) discuss a number of agricultural GHG emission mitigating practices, as well as barriers and enablers for their uptake. They find that economic incentives, voluntary approaches and provision of information are the most favorable approaches to overcome the barriers to adopting technically feasible mitigation practices. Considering the presented statements, we selected several measures and decisions that can potentially be implemented and have an effect on changes in agricultural GHG emissions. We started with assessing the effect of reaching the national target to manage 95% of the country's agricultural lands by 2020 (NDP, 2012). It is expected that reaching the target will cause a rapid raise in agricultural GHG emissions, as the area of cultivated land will increase. Another national target is to increase the share of area of certified organic farms to 10% in 2020 and additional 5% till 2030 (NDP, 2012). Reaching the organic farms' target can be achieved by increasing the agro-environmental support payments for organic farming practices. In addition, dissemination of information might be enhanced, thus stimulating reconsideration of farming practices. Increased area of organic farms is expected to lessen the amount of GHG emitted from soils. To assess the mitigation potential of GHG emissions from manure management systems, financial grants for installation of MMS and ADMS have been simulated. Additionally, dissemination of information for livestock farmers is simulated assuming that it would help in explaining the necessity and usefulness of the MMS. The instruments help in increasing the amount of manure managed, thus is expected that the released amount of GHG emissions into the atmosphere would decrease. In Latvia, renewable energy is supported by subsidizing renewable energy produced from bio-resources (biogas, solid biomass, and biofuels). The subsidies stimulate installation of technologies for production of renewable energy, which, on its turn, increases demand for

bio-resources. This has a direct effect on agriculture sector, namely, manure management and production of energy crops. The instrument causes a diverse effect on GHG emissions. The emissions are reduced when manure or crop residue is used for bioenergy production. Whereas, when energy crops are cultivated, the effect on emissions is negative as intensive application of synthetic fertilizers is practiced. In our model, the avoided GHG emissions from replacing fossil fuels for bio-resources are not considered, as they are accounted for energy sector. Finally, the effect of farmers' decision to increase the average yield of crops by applying larger amount of synthetic fertilizers (up to the maximum legal limit per hectare) was assessed. It was also tested, how an implementation of the nitrogen tax would affect the nitrogen application rates. As both measures are contradictory, the amount of GHG emitted depends on a farmers' net income. In the model, implementation or changes in the simulated decisions and measures are started in 2014–2016, depending on the measure applied. In the model's stock-and-flow diagrams (see Figs. S1–S3), the simulated measures and decisions are indicated with the gray rhombuses. The obtained results were compared with the target non-ETS GHG emission levels in 2020 and 2030, i.e. plus 17% and minus 30%, respectively, as compared to the level in 2005. Though, besides agriculture there are other sectors not covered by ETS (e.g. transport, waste management, and part of energy and industrial sectors), we assumed that each sector, namely agriculture, has to achieve the target levels individually. According to the Latvia's National Inventory Report (LNIR, 2014), in 2005, 2175.88 Gg CO2 eq. were emitted in the agricultural sector. Thus, the target levels are set to 2545.8 Gg CO2 eq. in 2020 and 1523.1 Gg CO2 eq. in 2030. 3. Results and discussion: the case of Latvia To analyze the influence of various policy measures and decisions on the agricultural GHG emissions, the results arising from variations of the parameter values used in the model were tested. First, the base scenario (reference scenario) was developed to observe the potential dynamics of the system provided that the agricultural sector will continue to develop following the historically observed tendency. This will happen in the influence of the existing support mechanisms. Then, based on the results of the base scenario, various decisions and measures were tested to assess their impact on GHG emission rates from agricultural sub-sectors — soils and manure management. Measures for abatement of GHG emissions from enteric fermentation (e.g. use of antimethanogenic additives or vaccines) were not simulated, as there is insufficient data for the region of the studied case to obtain reliable results. Finally, the impact of the tested measures on the development of the total agricultural GHG emissions was assessed and compared to the target levels. 3.1. Base scenario Results of the base scenario show the dynamics of the GHG emissions with the policy measures and decisions in force from 2005 until 2012 (back-casting), and the potential development until 2030, if nothing is changed in the existing policy design. The results indicate that the total agricultural GHG emissions will increase considerably and reach 2697 Gg CO2 eq. in 2020 (see Fig. 4). It has a 24% increase above the 2005 level, which means that the target of 2020 level cannot be achieved with the existing trend of development. The situation is even more unfavorable in 2030 when 3182 Gg CO2 eq. will be emitted twice exceeding the target level. The results show that the sharp increase in the total emissions will be caused mainly by the emissions from agricultural soils, as the emissions will increase by 73% between 2005 and 2030. The emissions are driven by expansion of cultivated agricultural land area (19% increase) and increase in total amount of synthetic fertilizers applied (almost threefold).

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GHG emissions in the agricultural sector, Gg CO2 eq.

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of agricultural soils. The national strategy documents imply the targets for increasing the percentage of managed agricultural land (to 95% in 2020) and the share of areas with organic farming certificate from the total managed area (10% in 2020 and 15% in 2030). Obviously, the targets have a direct effect on emissions from soils. The results obtained in the base scenario indicate that transition from conventional to certified organic farming practice will continue to develop, though on a slower rate, as requirements for obtaining a certificate are expected to reinforce. Hence, in 2030, the share of certified organic farming area will compose 13% of the total managed agricultural land, and the target share will not be reached. The target fraction of the overall land management also will not be reached as, in 2020, only 81% will be managed instead of the desired 95%. To test the influence of intensified management increase rate, it was assumed that the 95% target will be reached without considering what mechanisms will be necessary to achieve the increase. The results (denoted as “95% target” in Fig. 5) show that reaching the land management target will result in a slight increase of GHG emissions from agricultural soils (2–4% as compared to the base scenario). It is not only because of the increased fraction of managed land, but also due to the decreased share of organic farming area (12% in 2030). Considering that an increasing necessity exists for meeting the demand for food, feed, and energy crops, intensification of cropping was simulated. It was assumed that a decision to increase yield of crops by 1 ton per hectare is made. Since raise in cropping productivity implies an increase of the amount of fertilizers applied to soil, the resultant amount of GHG emissions increases considerably as compared to the base scenario (2–34% till 2030) (see Fig. 5).

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Fig. 4. Results of the base scenario (“2005” indicates the amount of GHG emitted in 2005, and “2020” and “2030” indicate the GHG emission target levels for the agricultural sector).

With the expected increase of livestock number emissions from enteric fermentation will increase by 14% till 2030. Whereas, emissions from manure management will slightly decrease (11%) as a result of support for bioenergy and installation of ADMS. 3.2. Effect on emissions from agricultural soils Several combinations of the decisions and measures were simulated to evaluate the potential effects on GHG emission levels from cultivation 3600 3200 2800 2400 2000 1600

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Fig. 5. Results of the effects of selected measures on the (a) total agricultural GHG emission dynamics, (b) GHG emission dynamics from agricultural soils, and (c) GHG emission dynamics from manure management.

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It is clear that abandoning the idea of streaming towards full utilization of the available resources, in this case the available agricultural land, is not a solution, and mechanisms mitigating the negative impacts (GHG emissions) have to be found. One such instrument is the nitrogen tax that would control nitrous oxide emissions in addition to limited groundwater nitrate loading. However, even simulating a substantial (ten-fold) increase of fertilizers' price did not result in a notable GHG emission reduction (see Fig. 5), and only up to 5.4% decrease was achieved. The reason is that the crop production costs that include also fertilizer costs are offset by revenues from crop sales. It suggests that the existing fertilizers' price is comparatively low, and even more substantial increase in price is required. In addition, as found also by Neufeldt and Schäfer (2008), the emission reduction is predominantly achieved reducing synthetic nitrogen fertilizers, whereas organic nitrogen from manure is practically unaffected. As discussed in Section 2.3, subsidizing bioenergy would cause an effect on the intensity of crop residue utilization, i.e. a larger fraction of crop residues would be removed from soils. Moreover, also cultivation of energy crops (e.g. rape and wheat) would rise. In addition, nutrients removed with crop residue would have to be replaced by synthetic fertilizers to ensure a sufficient fertility of soil. Simulating a double increase in subsidies for bioenergy shows that the effect would be adverse for agricultural sector, as the GHG emissions from soil would increase considerably — up to 12% as compared to the base scenario (see Fig. 5). Though, not modeled in our study, it is expected that the GHG emission reduction would be achieved in energy sector where fossil energy sources would be replaced by bio-resources. It can be concluded that the only measure that would cause a decrease in the growth rate of GHG emissions from soils is the nitrogen tax. Still, the effect would be rather imperceptible, as only 1–5% reduction would be achieved. Conversely, any decision made towards more intensive soil exploitation would cause an increase of the GHG emission level. This result suggests that other possible solutions should be sought and discussed in the near future to identify those measures and decisions that would provide benefits for climate and agricultural development, simultaneously. 3.3. Effect on emissions from manure management In Latvia, the necessity to assist farmers in installing MMS has been recognized, and funds have been allocated for the purpose starting from year 2007 (RDPL, 2006). As a result, more than 200 projects have been realized till 2014 and 300 more are planned until 2020. The projects realized till 2014 have been considered in the base scenario. Whereas, the effect of realizing the future projects was simulated by applying various measures for enhanced manure management systems (MMS) installation rate. In the past (2005–2014) there has been increasing tendency to install liquid storage and management systems, whereas the fraction of solid storage systems has decreased. In addition, starting from 2009 an increased interest in installing ADMS has been observed due to allocated funds for biogas plants. Particularly large fraction of manure has been transferred to ADMS in poultry farms, where 44% increase in ADMS has been reached just in three years' time. As a result, the total GHG emissions from manure management have descended (see Fig. 5). The simulation results show that, in the base scenario, similar emission trend will be observed till 2030, yet on a slower reduction rate. Fig. 5 shows the results of influence that various measures applied for management of manure have on GHG emission dynamics as compared to the base scenario. It can be seen that measures for MMS's installation projects (with exclusion of ADMS) induce the growth of emissions from manure management sector. It is caused by two factors, i.e. continued growth of the share of liquid manure management systems and decrease of manure deposited on pastures, paddocks and

ranges. Default emission factor for nitrous oxide emissions from liquid systems is lower than that for emissions from solid systems, i.e. 0.001 kg N2O-N per kg N and 0.02 kg N2O-N per kg N, respectively. Nevertheless, the resultant GHG emissions are slightly offset by methane emissions, as methane conversion factor is tenfold higher for liquid systems than that for solid systems (10% and 1%, respectively). In addition, it is projected that the fraction of manure deposited on pastures, paddocks and ranges will decrease as a result of changes in livestock farming practice. Hence, the amount of manure managed in MMS is expected to increase. A significant measure for installation of any MMS is the financial support. Simulation of the planned grants to be awarded to farmers (“Grant for MMS”) shows that GHG emissions from manure management will increase by 2–7% as compared to the base scenario. A substantial role in decision making towards installation of MMS would be played also by the availability of information. Simulating targeted information campaigns shows that the time to make the decision can be considerably decreased, if information is available to farmers on why and how MMS should be installed. Hence, the growth rate of the fraction of manure managed in MMS accelerates, and GHG emissions increase by 1–3.5% till 2030. For Latvia, abatement of GHG emissions from manure management sector would be achieved in the case of more intense installation of ADMS (see Fig. 5), since less methane and nitrous oxide emissions are released as compared to the other MMS. A grant covering 85% of anaerobic digestion management systems (ADMS) installation costs was simulated. It resulted in a significant 17% GHG emission abatement by 2030. Another instrument that would allow achieving GHG emission decrease is an increase of subsidies for supporting renewable energy generation from biogas produced by fermentation of manure. A 1–3% GHG emission reduction would be achieved by doubling the subsidy rate. Up to 20% reduction can be achieved by combining the two instruments — increased grants for ADMS and subsidies for bioenergy (see “ADMS + subsidies” in Fig. 5). Though accounted in the energy sector, GHG emissions are reduced also when the collected biogas is used by substituting fossil fuel (e.g. natural gas) for energy production. It can be concluded, that more effort should be devoted towards favoring installation of ADMS as larger amount of GHG emissions avoided can thus be provided for the manure management sector. In addition, increased support for production and utilization of bio-resources should be weighed, as installation of ADMS is thus indirectly motivated and GHG emissions reduced. Still, in the future modeling studies, energy market and price fluctuations of competitive fuels must be carefully analyzed to assess the full impact of fuel choice on changes in manure management practices.

3.4. Total effect on emissions from agricultural sector The effect of the measures providing GHG emission mitigation in the sub-sectors of agricultural soils or manure management was assessed with respect to changes in the total agricultural GHG emission level and the target values that should be reached by 2020 and 2030. The results show that none of the measures or their combinations tested allows achieving the targets (see Fig. 5). Though, subsidies for bioenergy provide some abatement of GHG emissions from manure management, the effect is offset by the emission increase from agricultural soils. Thus, the effect on the total agricultural GHG emissions is negative, i.e. emission increase is observed. Funds granted for installation of ADMS would limit the GHG emissions to 20.8% increase in 2020, as compared to 2005. Whereas, an increase of price of synthetic fertilizers by introducing a nitrogen tax would limit the emissions to 21% increase. Simulation of simultaneous implementation of the both instruments would result in limiting GHG emissions to 18.1% increase, thus nearing the closest to the target value of 2020, still not reaching it.

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3.5. Causal loop diagram

Causal loop diagrams are used as a basis for conceptualizing a representation of the system under study, or to investigate and/or draw conclusions about and visualize the nature of the relationship between the system's structure and dynamic behavior (Blumberga, 2010). Both, stock-and-flow diagram and causal loop diagram represent the same system, but in a different manner — the stock and flow diagram is a detailed, quantitative version of the model enabling simulation and analysis, whereas the causal loop diagram allows the visualization of the system's behavior in an easy and comprehensive way. The causal loop diagram in Fig. 6 shows the interacting feedback mechanisms (loops) between the parts of the modeled agricultural system. An increase of gross domestic product stimulates demand for food crops and animal products, and number of livestock and crop cultivation, respectively, is hence increased. Concomitantly, a larger area of agricultural land is necessary to be managed to meet the demand. In addition, direct and other payments are granted to farmers that motivate management of the agricultural land. With the overall managed area, the area of farms with conventional farming practice increases, hence increasing the total amount of synthetic fertilizers applied to soil. The total amount of nitrogen applied to soils grows inducing GHG emission rise. To respond to the increased GHG emission level, the society (decision makers) introduces a nitrogen tax. Time is necessary for decision makers to identify the need for a measure limiting GHG emissions, as well as to select and implement the proper instrument.

In system dynamics, causal loop diagrams are used to show the major feedback mechanisms by exhibiting the structure of the modeled system. The diagrams consist of elements of the studied system, arrows linking the elements and a “+” or “−” sign on each link that conveys information about the relationship between the elements (see Fig. 6). A positive link is between elements that change in the same direction, i.e. an increase of one element causes an increase of the other element. A negative link shows that elements change in the opposite direction, i.e. an increase of one element causes a decrease of the other element. A cross-marked link denotes a delay between cause and effect, i.e. time is necessary to observe the effect caused by an element. Also a complete feedback loop is given a designation. A loop denoted by “R” indicates a reinforcing loop — an initial disturbance leads to further change, thus a change in one direction causes even more a change in the same direction leading to exponential growth or collapse. A balancing feedback loop, denoted by “B”, has a stabilizing or goal seeking behavior — after a disturbance the system seeks to bring conditions into equilibrium state. When reinforcing and balancing loops are combined, the complex behavior of the system appears. In a sense, causal loop diagrams are as simplified maps of the connections in a closed loop system of cause and effect relationships.

+

Gross domestic product Support payments for eco-agriculture +

+

+ Consumption of animal products

Demand for food

+

B2

Share of certified organic farming

Installation of ADMS

Fraction of+ ADMS

+ Grant for ADMS +

B7

+ Demand for bioenergy

Bioenergy price

R3

+ Cultivation of food crops

B4 Subsidy for bioenergy

+

87

+

GHG + emissions

+ + Crop residue removal

B5

Cultivation of energy crops

+

+

+

Number of livestock

+ Grant for MMS

R2 +

+

Total N applied to soils + +

N tax

-

Area of conventional farming +

+

MMS capacity

+ Installation of MMS

B6 -

+ Synthetic fertilizers applied to soil

Price of synthetic fertilizers

+

Deficit of MMS capacity + + Amount of manure

Net income of farmers + +

+ B1

Average amount of synthetic fertilizers

Average yield of crops

Price of crops -

+ +

-

+

R1

+ Area of managed agricultural land + + +

+

Stock of crops

+

Cultivation of feed crops

Fig. 6. Causal loop diagram of the structure of the modeled agricultural system.

B3

Support payments

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Thereof, a considerable delay is created in the system's reaction to the negative impact it creates on environment. When the nitrogen tax is introduced, the price of synthetic fertilizers increases, thus decreasing the average amount of synthetic fertilizers applied to soil. It causes the average yield of crops to descend. As a result, the net income of farmers drops, and they lose the interest in land management. As the impact on environment (amount of GHG emissions released) lessens, the public attention becomes more relaxed and the impact of the policy measure (nitrogen tax) decreases. That, in turn, can lead to new environmental pollution problems which would call for a reinforcement of the solution (policy measure) implemented or finding a new solution. Hence, the balancing loop B1 is created (see Fig. 6), that tends to find the equilibrium state. The negative impact of the loop B1 is reduced by the positive effect of the balancing loop B2. The effect of support payments for ecoagriculture development allows increasing the area of managed agricultural land by increased share of organic farms. The area of conventional farms decreases and the amount of synthetic fertilizers applied — concomitantly. Simultaneously, the expansion of managed agricultural land is affected by the stock of crops, increase of which results from increased yield and cultivated area of a crop. The stock, on its turn, influences the market price of crops. As the price of crops decreases, the net income of farmers is decreased, thus lessening farmers' willingness to expand the area of land managed. Hence, the third balancing loop B3 is created. If the price of crops falls slower than the cost of crop cultivation is increased, then even higher area expansion rate is achieved. That causes a reinforcing adverse effect on the system, as larger area is cultivated and higher amount of synthetic fertilizers applied (reinforcing loop R1). GHG emission increase forces to look for alternative solutions with fewer emissions. Subsidies for bioenergy are allocated to stimulate substitution of fossil resources for bio-resources by decreasing the bioenergy price. In the agricultural sector, increased demand for bioenergy stimulates cultivation of energy crops, thus raising the amount of synthetic fertilizers applied to soil (balancing loop B4). In addition, enhanced removal of crop residue is induced. Crop residue contains nutrients, mineralization of which is important for ensuring nutrient balance of soil. When crop residue is removed for bioenergy generation, the nutrient balance is achieved by adding fertilizers to the soil (balancing loop B5). In the livestock sector, growth of number of livestock increases the GHG emissions from enteric fermentation. Also, the amount of manure produced increases. If MMS are not installed with sufficient pace to ensure management of the manure produced, the deficit of MMS's capacity grows (balancing loop B6). As inadequate management of manure increases GHG emissions, funding is allocated to minimize the deficit. Yet, the funding is granted with a delay, as time is necessary for decision makers to identify the need for MMS capacity increase to limit the GHG emissions. As the simulation results indicated, in the case of Latvia, the MMS's capacity increase would cause the GHG emissions to rise, thus reinforcing the negative effect (reinforcing loop R2). To minimize the effect, more funds are allocated for GHG emission abatement. Grants awarded for installation of ADMS increase the fraction of manure managed in ADMS, thus reducing the amount of GHG emitted from manure management (balancing loop B7). Whereas, subsidies for bioenergy launch even larger interest in ADMS's installation, as demand for the biogas produced in ADMS is enhanced (reinforcing loop R3). Finally, seven balancing and three reinforcing feedback loops are formed that characterize the system's behavior. The balancing loops describe processes that tend to be self-limiting and seek balances and equilibrium. Whereas, reinforcing loops describe unintended effects as, e.g. the increased GHG emissions from MMS. The causal loop diagram developed helps in understanding the elements interacting in the agricultural system, and emphasizes the complexity of achieving the desired effects on GHG emission mitigation. The unintended effects are caused by nonlinearities of the modeled system, as well as by the so-

called “policy resistance”, as defined by Sterman (2000). It is a situation, when policy instruments, measures or decisions are detained or poorly implemented due to unforeseen reaction of the system or its elements. The impact of policy instruments or measures can decrease if public attention becomes more relaxed due to a relative improvement (no further deterioration) of the environmental state (GHG emission level). That, in turn, can lead to new environmental pollution problems which would call for a reinforcement of the solution (policy measure) implemented or finding a new solution. The developed model and causal loop diagram help in identifying the links that counteract the positive intended effects. Therefore, we believe that the model can serve as a tool that would assist decision makers in selecting and testing instruments for GHG emission mitigation in the agricultural sector. Nevertheless, the developed model has some limitations. The model follows the IPCC guidelines for GHG inventories (IPCC, 2006), thus covering the environmental aspects related to global warming. The model also includes many economic factors. Though, for a profound understanding of interaction between agricultural activities, policy measures and social issues and the interaction's impact on agricultural GHG emissions the model should be developed further by including factors that characterize rural development, provision with qualified labor force, demand and consumption patterns of agricultural products etc. As limiting factors also some technical aspects could be added to the model (e.g. the availability of infrastructure or modernization of agricultural technologies and equipment). In the model, numeric values of certain parameters reflect the situation in Latvia. Also, emission factors specific for Latvian conditions are used in the model. In order to simulate agricultural GHG emissions in other countries, the model has to be adapted to those countries by changing the numeric values. Whereas, the basic structure of the model may remain unchanged. Overall, the developed model provides an insight into the impacts caused by policies on the agricultural sector and its GHG emissions, therefore can serve as a tool for decision making and policy planning. 4. Conclusions and further research A system dynamics model was developed in our study. Building the model is the first attempt to investigate potential feedbacks in an agricultural system with respect to the amount of greenhouse gases (GHG) emitted. The model may serve as a decision support tool for influence assessment of various measures and decisions on the system's GHG emission level. In our study, the model was used to assess the effects of various decisions and measures made in the agricultural sector of Latvia on GHG emission changes. The results obtained were compared to the non-ETS emission targets for 2020 and 2030, i.e. limiting emission increase to 17% in 2020, and reducing the emissions by 30% in 2030 as compared to 2005. The results of the base scenario showed that, in Latvia, agricultural GHG emission limitation targets will not be reached with continuation of the historically observed tendency of development. Moreover, the results of simulating selected decisions and measures demonstrated how complicated mitigation of GHG emissions in the agricultural sector would be. As demonstrated by the results of our study, unintended effects can be received by implementing some of the measures. Thus, e.g. supporting installation of manure management systems (MMS) would cause an increase of GHG emissions from manure management, as fraction of liquid storage systems would increase having higher methane and nitrous oxide emissions as compared to other MMS. The unintended effects are caused by nonlinearities of the complex agricultural system, as well as by unforeseen reaction of the system or its elements to measures implemented. In addition, in some cases the emissions are transferred from one sector to another. E.g. implementation of subsidies for bioenergy would reduce emissions in the energy sector, yet they would increase in the agricultural sector, as more energy crops would be cultivated. Therefore, emissions from agriculture should be viewed

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together with emissions from other non-ETS sectors to fully assess the effects various measures, decisions and instruments have on the overall GHG emission levels. It is evident that the non-ETS GHG emission mitigation targets cannot be reached in the agricultural sector of Latvia. Thereby, other nonETS sectors will have to reduce their emissions below the target levels to “swallow” the agricultural excess emissions. In our study, the model was applied to the case of Latvia. Nevertheless, the model can be applied to other countries with a similar structure of the agricultural system to analyze and implement policy measures for increased efficiency of GHG emission abatement. In addition, the model may be developed further by expanding the boundaries of the system, adding new elements and/or analyzing additional decisions and measures. Furthermore, the results obtained and conclusions made in our study reflect a behavior of an agricultural system representative for other countries with characteristics similar to Latvia. Further research may be directed at developing a more detailed model and scenarios that include the effects of various measures on nutrient cycles, as adoption of certain practices may reduce productivity of an agricultural system. Also, a sub-model might be developed that portrays the interaction between the agricultural sector and rural development, thus incorporating social aspects in the modeled system. Seeking for other instruments and measures that would stimulate agricultural GHG emission abatement should be continued. Acknowledgment Support for this work was provided by the Riga Technical University through the Scientific Research Project Competition for Young Researchers No. ZP-2014/17. The authors would also like to thank Professor Dagnija Blumberga for her guiding advice in the development of the model and policy scenarios. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2015.04.088. References Barisa, A., Romagnoli, F., Blumberga, A., Blumberga, D., 2015. Future biodiesel policy designs and consumption patterns in Latvia: a system dynamics model. J. Clean. Prod. 88, 71–82. Barlas, Y., 1996. Formal aspects of model validity and validation in system dynamics. Syst. Dyn. Rev. 12, 183–210. Blumberga, A., 2010. Basics of system dynamics modeling. In: Blumberga, A. (Ed.), Integration of System Dynamics Into Environmental Policy [In Latvian]. RTU VASSI, Riga, pp. 74–158. Blumberga, A., Timma, L., Romagnoli, F., Blumberga, D., 2015. Dynamic modelling of a collection scheme of waste portable batteries for ecological and economic sustainability. J. Clean. Prod. 88, 224–233. Bockel, L., Sutter, P., Touchemoulin, O., Jönsson, M., 2012. Using Marginal Abatement Cost Curves to Realize the Economic Appraisal of Climate Smart Agriculture Policy Options. Food and Agriculture Organisation of the United Nations, Rome, Italy (Available at: http://www.fao.org/docs/up/easypol/906/ex-act_macc_116en.pdf [Accessed on: 27.01.2015.]). Bush, B., Duffy, M., Sandor, D., Peterson, S., 2008. Using system dynamics to model the transition to biofuels in the United States. IEEE Int. Conf. Syst. Syst. Eng. 1–6 http:// dx.doi.org/10.1109/SYSOSE.2008.4724136. Chen, G.Q., Zhang, B., 2010. Greenhouse gas emissions in China 2007: inventory and input–output analysis. Energ Policy 38, 6180–6193. Colomb, V., Touchemoulin, O., Bockel, L., Chotte, J.-L., Martin, S., Tinlot, M., Bernoux, M., 2013. Selection of appropriate calculators for landscape-scale greenhouse gas assessment for agriculture and forestry. Environ. Res. Lett. 8, 1–10. Dace, E., Bazbauers, G., Berzina, A., Davidsen, P.I., 2014. System dynamics model for analyzing effects of eco-design policy on packaging waste management system. Resour. Conserv. Recycl. 87, 175–190. De Cara, S., Jayet, P.-A., 2011. Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement. Ecol. Econ. 70, 1680–1690. Denef, K., Paustian, K., Archibeque, S., Biggar, S., Pape, D., 2012. Report of greenhouse gas accounting tools for agriculture and forestry sectors. Interim Report to USDA Under Contract No. GS-23F-8182H (USA).

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